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The AAPG/Datapages Combined Publications Database
Showing 23,338 Results. Searched 200,673 documents.
An Introduction to Deep Learning: Part II
Lasse Amundsen, Hongbo Zhou, Martin Landrø
GEO ExPro Magazine
... often the model fails to predict the correct answer in their top five guesses (the top-5 error rate), in descending order of confidence. ILSVRC 2012...
2017
Simulating seismic data using generative adversarial networks
Bradley C. Wallet, Eyad Aljishi, Hussain Alfayez
International Meeting for Applied Geoscience and Energy (IMAGE)
... International Conference on Machine Learning, 70, 214–223. Chellapilla, K., S. Puri, and P. Simard, 2006, High performance convolutional neural...
2022
Seismic impedance inversion via neural networks and linear optimization algorithm
Bo Zhang, Yitao Pu, Ruiqi Dai, Danping Cao
International Meeting for Applied Geoscience and Energy (IMAGE)
..., and a low frequency model. The loss function of PINNs is designed to minimize the difference between real seismograms and synthetic seismic...
2024
Deep Learning Models for Methane Emissions Identification and Quantification
Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Dan O’Malley, Hari Viswanathan
Unconventional Resources Technology Conference (URTEC)
... to prepare the data for the machine learning model. In this section, we will outline the preprocessing and Convolutional Neural Network (CNN) model...
2023
Seismic Forward Modeling of Semberah Fluvio-Deltaic Reservoir
Adi Widyantoro, Wahyu Dwijo Santoso
Indonesian Petroleum Association
... modeling at each UKM wells to understand lithology and fluid effects over amplitude variations, 3) conceptual 2D convolutional model to understand boundary...
2021
Machine learning applications to seismic structural interpretation: Philosophy, progress, pitfalls, and potential
Kellen L. Gunderson, Zhao Zhang, Barton Payne, Shuxing Cheng, Ziyu Jiang, and Atlas Wang
AAPG Bulletin
... amplitude (grayscale) and fault probability from convolutional neural network (CNN) (red-white scale). The CNN model accurately predicts the steeply dipping...
2022
Deterministic and Statistical Wavelet Processing
Lee Lu
Southeast Asia Petroleum Exploration Society (SEAPEX)
... on the convolutional model for a seismic trace: it is assumed that an observed trace, x, is the convolution of an “effective wavelet”, w, with an “effective reflectivity...
1980
Refining our understanding of the subsurface geology using deep learning techniques
Salma Alsinan, Philippe Nivlet, Hamad Alghenaim
International Meeting for Applied Geoscience and Energy (IMAGE)
... Alghenaim, Unconventional Resources, Saudi Aramco Summary This work addresses the question surrounding the importance of the geological model used...
2022
Abstract: Kirchhoff Imaging with Adaptive Greens Functions for Compensation for Dispersion, Attenuation, and Velocity Imprecision; #90187 (2014)
Andrew V. Barrett
Search and Discovery.com
... the imaging at higher frequencies. Here I present a method for deriving and applying adaptively a short, white operator to compensate...
2014
4D Finite Difference Forward Modeling within a Redefined Closed-Loop Seismic Reservoir Monitoring Workflow, #41922 (2016).
David Hill, Dominic Lowden, Sonika, Chris Koeninger
Search and Discovery.com
...-field coupled dynamic integrated earth model to surface. From which 3D grids of petro-elastic parameters for a range of reservoir simulations...
2016
Methods of estimating wavelet stationarity, stabilizing non-stationarity, and evaluating its impact on inversion: A synthetic example using SEAM II Barrett unconventional model
Jesse Buckner, Michael Fry, Joe Zuech, Peter Harris, Bill Shea
International Meeting for Applied Geoscience and Energy (IMAGE)
... is simulated across a continuous 3D convolutional synthetic seismic volume, derived from the earth model of the SEAM II Barrett dataset. Multiple...
2023
Time-lapse full-waveform inversion by model order reduction using radial basis function
Haipeng Li, Robert G. Clapp
International Meeting for Applied Geoscience and Energy (IMAGE)
...Time-lapse full-waveform inversion by model order reduction using radial basis function Haipeng Li, Robert G. Clapp Time-lapse full-waveform...
2024
Automated active learning for seismic facies classification
Haibin Di, Leigh Truelove, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... convolutional neural networks have been popularly implemented for seismic image interpretation including facies classification, the performance...
2022
Accurate seismic data interpolation based on multiband intelligent training
Xueyi Sun, Benfeng Wang, Tongtong Mo
International Meeting for Applied Geoscience and Energy (IMAGE)
... information about subsurface structures and geological features. During the optimization of convolutional neural network (CNN)-assisted seismic data...
2023
Deep learning to predict subsurface properties from injected CO2 plume bodies using time-lapse seismic shot gathers
Son Phan, Wenyi Hu, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... without conventional velocity model building and imaging. A deep learning architecture with a new multi-branch design with different filtering sizes...
2022
Deep convolutional neural networks for generating grain-size logs from core photographs
Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott Cole, and Ishtar Barranco
AAPG Bulletin
...Deep convolutional neural networks for generating grain-size logs from core photographs Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott...
2022
Integrating U-net into full-waveform inversion for salt body building: A challenging case
Sixiu Liu, Abdullah Alali, Shijun Cheng, Tariq Alkhalifah
International Meeting for Applied Geoscience and Energy (IMAGE)
... shown by the white arrows in Fig. 3 (c), a second scale of FWI uses a larger TV value to invert the model (Fig. 3 (d)). We can see that the false...
2024
VSP Guided Reprocessing and Inversion of Surface Seismic Data
R. Gir, Dominique Pajot, Serge Des Ligneris
Southeast Asia Petroleum Exploration Society (SEAPEX)
... seismic data is known as the “convolutional model of the seismogram”. This model states that after proper data processing, the final seismic data has...
1988
Noise suppression and compressive sensing recovery with seismic-adapted DnCNN within RED
Nasser Kazemi
International Meeting for Applied Geoscience and Energy (IMAGE)
... is an additive white Gaussian noise. In this model, DnCNN acts as a noise-estimating operator L (m) ⇡ n, and s ⇡ m L (m), (5) where L (·) is the DnCNN...
2024
Abstract: Recovering Low Frequencies for Impedance Inversion by Frequency Domain Deconvolution; #90224 (2015)
Sina Esmaeili and Gary Frank
Search and Discovery.com
... reflectivity. We start by reintroducing the convolutional model for normal incident seismograms and then show how reflectivity can be estimated...
2015
Sparse time-frequency representation based on Unet with domain adaptation
Yuxin Zhang, Naihao Liu, Yang Yang, Zhiguo Wang, Jinghuai Gao, Xiudi Jiang
International Meeting for Applied Geoscience and Energy (IMAGE)
... propose the sparse time-frequency representation (STFR) based on Unet with domain adaptation (STFR-UDA) model for solving these issues. First, we...
2022
Noise analysis and ML denoising of DAS VSP data acquired from ESP lifted wells
Ge Zhan, Yao Zhao, Cheng Cheng, Josef Heim, Weihong Fei, Mike Craven, Scott Baker, Gilles Hennenfent
International Meeting for Applied Geoscience and Energy (IMAGE)
... developed a machine learning (ML) workflow that uses a deep convolutional U-Net architecture to model the ESP noise first and then subtract it from...
2022
Post Migration Processing of Seismic Data
Dashuki Mohd.
Geological Society of Malaysia (GSM)
... or multiples. The basis for deconvolution is the convolutional model (Robinson, 1984). In the convolutional model, a seismic trace is viewed...
1994
Machine learning-based residual moveout picking
Farhad Bazargani, Wenjun Zhang, Anu Chandran, Zaifeng Liu, Harry Rynja
International Meeting for Applied Geoscience and Energy (IMAGE)
... in the migration velocity model. Accurate and efficient RMO picking is the key to the success of tomographic velocity model building workflows. Conventional RMO...
2022
Seismic reflectivity inversion via a regularized deep image prior
Hongling Chen, Mauricio D. Sacchi, Jinghuai Gao
International Meeting for Applied Geoscience and Energy (IMAGE)
... assist in characterizing the subsurface. By adopting the stationary convolution model, seismic reflectivity inversion is posed as a multichannel deblurring...
2022